Adaptive Event-triggered Formation Control of Autonomous Vehicles

TL;DR

This paper introduces adaptive event-triggered control strategies for autonomous vehicle formations, enhancing safety and efficiency by reducing communication and control updates in complex traffic scenarios.

Contribution

It develops a novel adaptive event-triggered framework with a sampling-based observer and multiple mechanisms, improving formation control under uncertainties and reducing control update frequency.

Findings

Effective in narrow passages and obstacle avoidance

Reduces control updates while maintaining formation accuracy

Ensures stability and avoids Zeno behavior

Abstract

This paper presents adaptive event-triggered formation control strategies for autonomous vehicles (AVs) subject to longitudinal and lateral motion uncertainties. The proposed framework explores various vehicular formations to enable safe and efficient navigation in complex traffic scenarios, such as narrow passages, collaborative obstacle avoidance, and adaptation to cut-in maneuvers. In contrast to conventional platoon control strategies that rely on predefined communication topologies and continuous state transmission, our approach employs a sampling-based observer to reconstruct vehicle dynamics. Building upon an adaptive backstepping continuous-time controller, we design three distinct event-triggered mechanisms, each offering a different trade-off between formation tracking performance and control efficiency by reducing the frequency of control signal updates. A Lyapunov-based stability analysis is conducted to guarantee bounded tracking errors and to avoid Zeno behavior. Finally, the proposed event-triggered controllers are validated through simulations of vehicular formation in three scenarios, highlighting their impact on traffic safety and mobility.